In this week we finalize our neural network model and share results.

https://realpython.com/python-ai-neural-network/

Introduction

This week we will share our neural network prediction results and the challenging process to get these results. Basically, we calculated the mean great circle distance error after predicting longitude and latitude values by using Multi-Layer Perceptron Regression.

We also found some solutions for the problems we mentioned in previous weeks. We are happy to announce that we get a better mean great circle distance error than our related work had. …


In this glorious week, we want to talk about neural network implementation in predicting the geographical origin of music.

https://realpython.com/learning-paths/machine-learning-python/

Introduction

Today we will talk about our neural network model, algorithms, and methods that we use to find the origin of music around the world.

Feature Importance

Feature importance is a really important area for neural networks. The topics such as which variables are mostly used to make predictions, the existence of correlations, possible causal relationships, help us understand the success of neural networks in mimicking real intelligence. Because we humans also consider these relationships in the decisions we make in our real lives.


At the very beginning of this wonderful week, we decided to do something new to solve this problem.

https://realpython.com/courses/introduction-pandas-and-vincent/

New Horizons

Until this week, we have predicted these countries by converting the raw longitude and latitude data in the dataset into country labels and we got relatively low accuracy because the dataset was not distributed homogeneously and the target label(country) to be predicted was too many.

Although its accuracy is relatively low, we think we have exceeded a certain threshold in predicting target categories(countries) despite all the difficulties. …


Today we want to share results of prediction under the application of some machine learning algorithms. We got results from Random Forest Classifier, Logistic Regression, Support Vector Machines.

https://realpython.com/

INTRODUCTION

First of all, we approached the problem which is finding the origin of music as a classification problem. We convert longitude and latitude values to the country labels as below.


We spent this week diving deeper into the related works and understanding what is going on with data.

Team: Muhammet Subaşı, Harun Bürkük, Mustafa Korkmazlar

source: https://www.boldbi.com/blog/visualize-geographical-data-using-map-dashboard

Firstly, if we talk about why we choose this dataset, this dataset promise challenge because of provides working with geospatial data. Geospatial data (also known as “spatial data”) is used to describe data that represents features or objects on the Earth’s surface. Whether it’s man-made or natural, if it has to do with a specific location on the globe, it’s geospatial.

Analyzing Related Work

Our related paper writers started with describe the problem. They decided to cast the…


Music is a worldwide communication tool that is accepted as a common language of people all around the world. In the world, there are a huge variety of types of music. The reasons for this variety are historical, cultural, and geographical diversity. Almost every geographic region has its own characteristics and melodies. In this project, we will try to accurately predict the origins of the songs by looking at sound wave characteristics retrieved from the audio files of songs.

World and Music

Dataset

In this project, we will use the Geographical Original of the Music Data Set from UCI. This dataset contains 1059 rows…

Muhammet Subaşı

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